Integration of CFD analysis and artificial neural networks for estimation of erosion rate in oil and gas pipelines
Transporting oil and gas via pipelines has been creating countless challenges; sand particles deposit and erode the pipelines wall. Generally, conventional erosion rate prediction models are conservative due to numerous generalizations and theories. Finite Element Analysis via computational fluid dy...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024015421 |
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| Summary: | Transporting oil and gas via pipelines has been creating countless challenges; sand particles deposit and erode the pipelines wall. Generally, conventional erosion rate prediction models are conservative due to numerous generalizations and theories. Finite Element Analysis via computational fluid dynamics (CFD) approach has proven to be useful to solve sand deposition problems and to estimate the eroded pipe due to its capability to precisely determine erosion deficiencies and make predictions with great precision. In this study, a CFD model was developed to calculate sand erosion rates in a 2-inch, 90° elbow pipe. Effects of particle sizes, sand flowrates, fluids velocities and pipe diameters on erosion rates were studied. The model was validated against literature data, and it was then used to generate data via sensitivity analysis simulations. The data became the basis for developing artificial neural network (ANN) models, which were then deployed in the environment of a process simulation software called Symmetry. Based on this approach, a variety of pipelines can be modelled, and maximum erosion rates of pipelines are calculated using deployed ANN models. Hence, a comprehensive study on the fluid flow dynamics and erosion rates of the pipelines can be evaluated simultaneously. |
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| ISSN: | 2590-1230 |